안드로이드 모바일 악성 앱 탐지를 위한 확률적 K-인접 이웃 분류기

Abstract

ABSTRACT In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user’s information by installing the malware in the user’s smar tphone without the user’s authority. This kind of action are th reats to the user’s privacy. The malware characteristics are different to the general applications. It requires the user’s authority . In this paper, we proposed a new classification method of user requirem ents method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) method s. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision o f PKNN is improved compare to the previous K-Nearest Neighbor(KNN ). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parame ter k and  . Also the sample that has being use in this experiment is based on the Contagio.Keywords: Malware Detection, Android Permissions, Principal Component Ana lysis, KNN, PKNN